Just how good is your customer data? 3 key qualities to consider

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“If you have the right data and know how to use it, you win.” -3Q CEO David Rodnitzky

Digital marketing is evolving. The shift in focus from channels and products, to customer preferences and experiences, is driving a new model with Customer Accountability as its central theme and guiding principle. At the core of this model are customer data and insights that provide a deeper, richer view of what occurs across the entire customer journey. Knowing where and how to find, access, and collect customer data becomes extremely important. And the ability to validate customer data through real-time testing, and leverage test results and insights to create better user experiences and optimize conversion rates, has become somewhat of a holy grail.

But let’s start with customer data itself: knowing where and how to access customer data is important, but understanding specific characteristics about the customer data we collect is equally as important. Understanding where, when, and how data are collected will inform us on data quality and will allow us to leverage that data in the most useful and compelling ways in our digital advertising practices. And familiarizing ourselves with specific qualities of our audience data are essential to the success customer-focused marketing.

The three characteristics of audience data that we need to understand are: when it originated (freshness); where it originated (i.e. is it 1st-, 2nd-, or 3rd-party data); and how it was collected (intent). We’ll dive into each below.

Data Freshness

Data freshness, or whenwe collect customer data, has obvious impacts on the quality of these data and the insights we can glean from them. Stale data is usually useless data. So understanding issues around latency and date ranges for our customer data models is critical in defining how fresh and useful data is. Data freshness is dependent on the type of product or service being marketed as well; if someone searched on a keyword related to solar panels, I’d give that prospect a relevancy window of a few weeks or even longer before I would consider him/her stale. If I’m selling flowers a week before Valentine’s Day, I’d consider a much shorter window.

Amazon and other successful eCommerce sites leverage 1st-party data effectively to craft predictive shopping experiences based on what a customer might want to buy after or along with an existing purchase. Google offers powerful tools for helping to leverage 1st-party data for campaign efficiencies such as Customer Match Remarketing Lists for Search Ads (RLSA). (They offer their own aggregate data through Similar Audiences Demographics for Search Ads.) Facebook, of course, has led the charge with 1st-party data tools for advertisers, including Customer Audiences and Lookalike Audiences. Along with engaging current customers, these tools can help marketers dramatically expand customer reach, by finding audiences with characteristics similar to those we identify in our current remarketing lists.

There are savvy ways to leverage 2nd-party data for marketing as well. Second-party data is basically any data that comes along with media-buying activities, such as a customer’s location, their mobile device model or OS, a website or app where an ad opportunity is, etc. These data points can be used in segmenting audiences into categories for more targeted campaigns.

Third-party data is data purchased, usually on a large scale, from 3rd-party data management platforms, or DMPs. This data is then sold to marketers. There are data aggregators, such as Bluekai, Lotame, Datalogix, and Adobe Audience Manager, who collect, sort, store, and sell 3rd-party data. The benefit of this DMP data is the sheer amount of it that is available – and that it usually offers insights into audience behavior and demographics. The downside to 3rd-party data is that it isn’t free or exclusive — our competitors can also purchase and access it.

Intent: Declared vs. Revealed

Finally, knowing how to interpret different data types and map these data sets to customer intent is one of the most important components to customer-focused marketing. And a key distinction we need to make when mapping and understanding customer intent is the difference between declared and revealed intent.

Data we gather directly through surveys or interviews, or indirectly from mining social media, offers us extremely important insight into what people are thinking and saying in public. Yet this declared data is going to be very different from data we collect from, say, search, which is a very private activity. What customers search on in the privacy of their homes or mobile device screens points more accurately to revealed intent. And this is likely a more accurate source of customer intent.

“Declared preferences are what people say they’ll do and revealed preferences are what they actually do. Yes, the gap can be very wide – depending, of course, on the situation. It’s not that we lie necessarily; it’s just that our behavior doesn’t always follow our stated intentions, especially when our behavior isn’t being observed… the worst data are generally self-reported data; the best are data that come from actual behavior and can be verified.” (from an interview with IBM Senior Writer Kelsey Howarth)

All of these data qualifiers are essential to understanding just how valuable your customer data actually is. In a follow-up post, I’ll explain why conversion rate optimization (CRO) can do more than optimize your traffic; it can be an incredibly effective validator of your customer data.

About the author

Aaron Bart

Aaron is 3Q Digital’s Vice President of Creative Services; he specializes in UX design, creative testing and conversion optimization. He has 16 years of experience in digital advertising, with a focus on conversion path optimization and analytics. Prior to joining 3Q, Aaron was Director of Creative Services at iProspect, where he led design, development and CRO efforts for Intel, ADP, Hilton Honors, Lenovo, Wells Fargo, Epicor, and other brands. Previously he co-managed the Interactive group at Y&R San Francisco, where he was responsible for developing and executing digital advertising campaigns for brands such as Microsoft, Hitachi Data Systems, Dr Pepper, and 7UP. Aaron received his BA from Oberlin College and his MA from Yale University.